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In-Sensor-Memory Computing for Post-Von Neumann Intelligence: A Perspective
Hongyu Tang1,2, Ninghai Yu3, Pengsheng Min3
1College of Intelligent Robotics and Advanced Manufacturing, Fudan University, Shanghai, 200433, People's Republic of China. hongyu_tang@fudan.edu.cn.
Nano-Micro Letters
|April 19, 2026
Summary
In-sensor-memory computing (ISMC) overcomes limitations of traditional architectures by integrating processing and memory. This approach enables energy-efficient, distributed artificial intelligence at the data source.
Area of Science:
- Computer Science
- Materials Science
- Electrical Engineering
Background:
- Conventional von Neumann architectures face limitations due to data movement between sensing, memory, and computation.
- The slowing of transistor scaling necessitates architectural innovation for advancing intelligent systems.
Purpose of the Study:
- To survey technological foundations, architectural trends, and applications of In-Sensor-Memory Computing (ISMC).
- To examine global collaborations and identify challenges in ISMC development.
Main Methods:
- Co-locating perception, storage, and computation within unified device and system architectures.
- Leveraging advances in memristive/ferroelectric devices, novel materials, 3D integration, and neuromorphic architectures.
- Utilizing co-evolved algorithms like spiking neural networks and reservoir computing.
Main Results:
- In-sensor-memory computing (ISMC) enables in situ signal processing, mixed-signal computation, and event-driven intelligence.
- Recent technological and algorithmic advancements have expanded the capabilities of ISMC platforms.
- ISMC offers a pathway to overcome the bottlenecks of traditional computing architectures.
Conclusions:
- ISMC is a promising post-von Neumann hardware paradigm for energy-efficient, distributed intelligence.
- Addressing challenges in variability, reliability, scalability, and benchmarking is crucial for ISMC's advancement.
- ISMC is poised to revolutionize intelligent systems by enabling computation at the data source.
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